
NUQE [1]} uses a lookup layer containing embeddings for target words and their source-aligned words. The embeddings are concatenated and fed into two sets of feed-forward layers and a Bi-GRU layer. NUQE does not require training on parallel corpora.

PredEst [2]} is a OpenKiwi implementation [3]} of the predictor-estimator framework, which uses a bidirectional LSTM and two unidirectional LSTMs as the predictor, and the estimator takes features produced by the predictor and makes the predictions.

XLM-PredEst and Bert-PredEst [4]} combine pre-trained language models with the predictor-estimator framework through transfer learning approaches. Pre-trained XLM and Bert models are utilized respectively in these two baseline methods.

Bilingual Expert [5]} is a QE system proposed by [5]}. They build a conditional target language model with a bidirectional transformer and propose to extract mis-matching features from the predictor. A Bi-LSTM model is used as the estimator.

